As adoption of 5G service grows worldwide, the network is continuously evolving to accommodate new services, business models and ecosystem partnerships. To keep up with the rapid pace, private 5G and mobile network operators (MNOs) need adaptive approaches to problem solving that include fast and accurate root cause identification and remediation.
However, with so much data available today, it’s nearly impossible to sift through network events, alarms and obscure behaviors whenever a problem occurs. Compounding that is the fact that many network operation centers (NOCs) are still monitoring network behaviors manually. That means that each time a threshold is crossed, the system has to notify a person who may, or may not, be available to manually investigate the issue and take corrective action. Further complicating the challenge at hand is the significant time and resource allocation required during manual review of network issues.
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As a result, network operations teams are struggling to keep up with the challenges of operating a more complex network. Big data abstracted from the network requires greater adoption of automated technologies such as machine learning (ML) and artificial intelligence (AI) to alleviate the manual challenges.
Tipping Point
Telecoms networks have come a long way from being just a ‘dumb’ conduit for ‘smart’ data. The network is able to gather large quantities of real-time data automatically — information about performance, detailed usage and the network itself. This is good news for operators, as this data can yield valuable insights to inform impactful and profitable decisions.
Yet, we have reached the point where NOCs have more data than they know what to do with, stored in more places than the organization can effectively access.
The ability to consume, process and analyze network data has now outpaced spreadsheets, traditional databases and even complicated data visualization tools and applications. Making use of this data can be expensive and complex, requiring vast expertise to glean intelligent insights that can be effectively monetized.
With the latest advancements in network analytics, AI and ML can be leveraged to build, train and interpret network models that are capable of emulating human experiences.
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In fact, the use of automated network models enables extremely granular network monitoring that no human being is capable of doing, helping to reduce the number of catastrophic events over time. This allows MNOs and private 5G network operators to focus their time and energy on improving customer experiences, while day-to-day operational tasks are managed by an AI system with a neural network brain.
Model Behavior
Long short term memory (LSTM) is a recurrent neural network model with memory blocks that provides context for the information that a system receives, helping to inform next steps. With the use of LSTM and the mathematical models that make up time series prediction, ML can analyze all the information a network creates, forecast future behaviors, and enforce policies that are designed to prevent service disruptions.
In this way, network information can be used to build and maintain multi-dimensional neural network models. Maintaining these network models includes model training, which is critical to maintaining the accuracy of behavioral predictions.
For example, streaming data analysis and online/offline model training can be used to forecast network circuit and port utilization. Combining utilization predictions and forecasts with latency monitoring could proactively identify path fluctuations in longer routes that might impact service for latency sensitive applications. Once identified, NOC staff might choose to provision a new circuit, re-provision an existing circuit with fewer fluctuations, or even realistically assess applications that appear to be unnecessarily latency sensitive.
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Right Place at the Right Time
With adaptive network solutions powered by ML and AI, operations teams can solve network problems faster, inevitably reducing troubleshooting time and creating greater efficiencies. However, knowing where to start is key to maximizing effectiveness. By learning where to look for problems that lend themselves to automated solutions, network managers can then use that knowledge to solve those problems more quickly and economically. Following are several real-world examples of how AI/ML can be used to manage today’s networks.
Predictive Network Planning
Predictive network planning allows operators to reduce downtime by pinpointing exactly where resources should be allocated – either by making use of idle capacity, or investing in new equipment. Rather than relying on traditional metrics like capacity and reach, MNOs can leverage AI/ML tools to optimize capital expenditure (CapEx) budgets for customer experience and new service delivery, driving revenue growth to deliver higher return on investment (ROI).
Consistent Problem Solving
Solutions to network problems should not depend on the experience of the person who catches the ticket. In order to improve the consistency of problem solving, NOCs need consistent measurements and responses to return the network to steady-state convergence on the back end of network events, like planned maintenance or an outage.
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Adaptive Network Solutions
Complex 5G networks require fast and accurate root cause identification and remediation. Using multi-dimensional observations from the network itself, transformative analytics and network automation delivers carefully orchestrated remediation to enable adaptive network management solutions.
Machine learning enables automatic identification and sorting of relevant data, classifying it appropriately and pinpointing systemic issues quickly. For example, an operator can classify systemic issues, or events, that occur on the network based on multi-dimensional data sets. As the classifier system is executed against live data, notifications and/or automated actions can take place when system data encroaches within a classification. Classification systems could be derived from common events, vendor hardware, circuits and other domains. Having this ability allows operations teams to use testing outputs to choose the best solution for the specific problem at hand, and then the system re-provisions the network automatically.
Monitor Rising Alarm Storms
Insights and remediation related to network degradation and failures inevitably cascade into major usability problems for customers, often creating an ‘alarm storm’ with no obvious indication where to start solving the problem. Trying to manually sort through all the relevant data to find the root cause would take days, whereas ML anomaly detection and AI intelligence can surface those events faster, identify root causes more quickly, and provide suggested resolutions within hours, not days.
Recognize Disruptive Behaviors
Once a catastrophic event has been resolved, intelligence in the network enables operators to better understand what behaviors contributed to that event, and investigate those behaviors for future reference and preemptive remediation. This means that a specific event, such as a fiber cut or equipment failure, can be tagged with potential resolution suggestions for similar future events, allowing the network to ‘learn’ from the past.
Automated Maintenance
AI and ML techniques can be used to track network measurements and events over time, analyzing changes, trends, seasonality, cycles and fluctuations. Using ML to isolate issues, AI then correlates the data, comes to conclusions, and triggers network automation to deliver a closed loop system that fixes root causes before they severely impact end-user experience.
Monetize Network Data
With AI/ML solutions that enable management of the entire data pipeline of a multi-layer network, MNOs can monetize network data to deliver new service offerings and build customer loyalty through improved service quality. This provides much greater sophistication in pricing and consumer segmentation as it relates to network usage, enabling dynamic, on-demand capabilities like partitioning and prioritizing traffic, managing high-traffic users that impact network service quality, or setting pricing structures based on different types of traffic.
Dawn of a New Age with AI-powered Brain
As network technology has evolved, network behaviors are experiencing significant changes and fluctuations, driving greater complexity in the interdependencies between data and the optimization of network operations for peak performance. The ability to analyze and extract meaningful insights from real-time network data is key to making informed, impactful decisions that ensure the best possible customer experience and maximum ROI.
With so much data available today, only advanced network automation powered by ML and AI tools can close the gaps and tame the data tsunami — otherwise operations teams will find themselves drowning in data and debt.
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